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Building a Lemmatizer and a Spell-checker for Sorani Kurdish | The present paper aims at presenting a lemmatization and a word-level error
correction system for Sorani Kurdish. We propose a hybrid approach based on the
morphological rules and a n-gram language model. We have called our
lemmatization and error correction systems Peyv and R\^en\^us respectively,
which are the first tools presented for Sorani Kurdish to the best of our
knowledge. The Peyv lemmatizer has shown 86.7% accuracy. As for R\^en\^us,
using a lexicon, we have obtained 96.4% accuracy while without a lexicon, the
correction system has 87% accuracy. As two fundamental text processing tools,
these tools can pave the way for further researches on more natural language
processing applications for Sorani Kurdish.
| 2,018 | Computation and Language |
Patient Risk Assessment and Warning Symptom Detection Using Deep
Attention-Based Neural Networks | We present an operational component of a real-world patient triage system.
Given a specific patient presentation, the system is able to assess the level
of medical urgency and issue the most appropriate recommendation in terms of
best point of care and time to treat. We use an attention-based convolutional
neural network architecture trained on 600,000 doctor notes in German. We
compare two approaches, one that uses the full text of the medical notes and
one that uses only a selected list of medical entities extracted from the text.
These approaches achieve 79% and 66% precision, respectively, but on a
confidence threshold of 0.6, precision increases to 85% and 75%, respectively.
In addition, a method to detect warning symptoms is implemented to render the
classification task transparent from a medical perspective. The method is based
on the learning of attention scores and a method of automatic validation using
the same data.
| 2,018 | Computation and Language |
Adaptive Input Representations for Neural Language Modeling | We introduce adaptive input representations for neural language modeling
which extend the adaptive softmax of Grave et al. (2017) to input
representations of variable capacity. There are several choices on how to
factorize the input and output layers, and whether to model words, characters
or sub-word units. We perform a systematic comparison of popular choices for a
self-attentional architecture. Our experiments show that models equipped with
adaptive embeddings are more than twice as fast to train than the popular
character input CNN while having a lower number of parameters. On the
WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5
perplexity compared to the previously best published result and on the Billion
Word benchmark, we achieve 23.02 perplexity.
| 2,019 | Computation and Language |
The Rule of Three: Abstractive Text Summarization in Three Bullet Points | Neural network-based approaches have become widespread for abstractive text
summarization. Though previously proposed models for abstractive text
summarization addressed the problem of repetition of the same contents in the
summary, they did not explicitly consider its information structure. One of the
reasons these previous models failed to account for information structure in
the generated summary is that standard datasets include summaries of variable
lengths, resulting in problems in analyzing information flow, specifically, the
manner in which the first sentence is related to the following sentences.
Therefore, we use a dataset containing summaries with only three bullet points,
and propose a neural network-based abstractive summarization model that
considers the information structures of the generated summaries. Our
experimental results show that the information structure of a summary can be
controlled, thus improving the performance of the overall summarization.
| 2,018 | Computation and Language |
SALSA-TEXT : self attentive latent space based adversarial text
generation | Inspired by the success of self attention mechanism and Transformer
architecture in sequence transduction and image generation applications, we
propose novel self attention-based architectures to improve the performance of
adversarial latent code- based schemes in text generation. Adversarial latent
code-based text generation has recently gained a lot of attention due to their
promising results. In this paper, we take a step to fortify the architectures
used in these setups, specifically AAE and ARAE. We benchmark two latent
code-based methods (AAE and ARAE) designed based on adversarial setups. In our
experiments, the Google sentence compression dataset is utilized to compare our
method with these methods using various objective and subjective measures. The
experiments demonstrate the proposed (self) attention-based models outperform
the state-of-the-art in adversarial code-based text generation.
| 2,019 | Computation and Language |
MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for
Task-Oriented Dialogue Modelling | Even though machine learning has become the major scene in dialogue research
community, the real breakthrough has been blocked by the scale of data
available. To address this fundamental obstacle, we introduce the Multi-Domain
Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human
written conversations spanning over multiple domains and topics. At a size of
$10$k dialogues, it is at least one order of magnitude larger than all previous
annotated task-oriented corpora. The contribution of this work apart from the
open-sourced dataset labelled with dialogue belief states and dialogue actions
is two-fold: firstly, a detailed description of the data collection procedure
along with a summary of data structure and analysis is provided. The proposed
data-collection pipeline is entirely based on crowd-sourcing without the need
of hiring professional annotators; secondly, a set of benchmark results of
belief tracking, dialogue act and response generation is reported, which shows
the usability of the data and sets a baseline for future studies.
| 2,020 | Computation and Language |
Specificity measures and reference | In this paper we study empirically the validity of measures of referential
success for referring expressions involving gradual properties. More
specifically, we study the ability of several measures of referential success
to predict the success of a user in choosing the right object, given a
referring expression. Experimental results indicate that certain fuzzy measures
of success are able to predict human accuracy in reference resolution. Such
measures are therefore suitable for the estimation of the success or otherwise
of a referring expression produced by a generation algorithm, especially in
case the properties in a domain cannot be assumed to have crisp denotations.
| 2,018 | Computation and Language |
Text Morphing | In this paper, we introduce a novel natural language generation task, termed
as text morphing, which targets at generating the intermediate sentences that
are fluency and smooth with the two input sentences. We propose the Morphing
Networks consisting of the editing vector generation networks and the sentence
editing networks which are trained jointly. Specifically, the editing vectors
are generated with a recurrent neural networks model from the lexical gap
between the source sentence and the target sentence. Then the sentence editing
networks iteratively generate new sentences with the current editing vector and
the sentence generated in the previous step. We conduct experiments with 10
million text morphing sequences which are extracted from the Yelp review
dataset. Experiment results show that the proposed method outperforms baselines
on the text morphing task. We also discuss directions and opportunities for
future research of text morphing.
| 2,018 | Computation and Language |
Neural Entity Reasoner for Global Consistency in NER | We propose Neural Entity Reasoner (NE-Reasoner), a framework to introduce
global consistency of recognized entities into Neural Reasoner over Named
Entity Recognition (NER) task. Given an input sentence, the NE-Reasoner layer
can infer over multiple entities to increase the global consistency of output
labels, which then be transfered into entities for the input of next layer.
NE-Reasoner inherits and develops some features from Neural Reasoner 1) a
symbolic memory, allowing it to exchange entities between layers. 2) the
specific interaction-pooling mechanism, allowing it to connect each local word
to multiple global entities, and 3) the deep architecture, allowing it to
bootstrap the recognized entity set from coarse to fine. Like human beings,
NE-Reasoner is able to accommodate ambiguous words and Name Entities that
rarely or never met before. Despite the symbolic information the model
introduced, NE-Reasoner can still be trained effectively in an end-to-end
manner via parameter sharing strategy. NE-Reasoner can outperform conventional
NER models in most cases on both English and Chinese NER datasets. For example,
it achieves state-of-art on CoNLL-2003 English NER dataset.
| 2,018 | Computation and Language |
An Incremental Iterated Response Model of Pragmatics | Recent Iterated Response (IR) models of pragmatics conceptualize language use
as a recursive process in which agents reason about each other to increase
communicative efficiency. These models are generally defined over complete
utterances. However, there is substantial evidence that pragmatic reasoning
takes place incrementally during production and comprehension. We address this
with an incremental IR model. We compare the incremental and global versions
using computational simulations, and we assess the incremental model against
existing experimental data and in the TUNA corpus for referring expression
generation, showing that the model can capture phenomena out of reach of global
versions.
| 2,018 | Computation and Language |
Parameter-free Sentence Embedding via Orthogonal Basis | We propose a simple and robust non-parameterized approach for building
sentence representations. Inspired by the Gram-Schmidt Process in geometric
theory, we build an orthogonal basis of the subspace spanned by a word and its
surrounding context in a sentence. We model the semantic meaning of a word in a
sentence based on two aspects. One is its relatedness to the word vector
subspace already spanned by its contextual words. The other is the word's novel
semantic meaning which shall be introduced as a new basis vector perpendicular
to this existing subspace. Following this motivation, we develop an innovative
method based on orthogonal basis to combine pre-trained word embeddings into
sentence representations. This approach requires zero parameters, along with
efficient inference performance. We evaluate our approach on 11 downstream NLP
tasks. Our model shows superior performance compared with non-parameterized
alternatives and it is competitive to other approaches relying on either large
amounts of labelled data or prolonged training time.
| 2,019 | Computation and Language |
Automatic Evaluation of Neural Personality-based Chatbots | Stylistic variation is critical to render the utterances generated by
conversational agents natural and engaging. In this paper, we focus on
sequence-to-sequence models for open-domain dialogue response generation and
propose a new method to evaluate the extent to which such models are able to
generate responses that reflect different personality traits.
| 2,018 | Computation and Language |
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question
Answering | Recently, open-domain question answering (QA) has been combined with machine
comprehension models to find answers in a large knowledge source. As
open-domain QA requires retrieving relevant documents from text corpora to
answer questions, its performance largely depends on the performance of
document retrievers. However, since traditional information retrieval systems
are not effective in obtaining documents with a high probability of containing
answers, they lower the performance of QA systems. Simply extracting more
documents increases the number of irrelevant documents, which also degrades the
performance of QA systems. In this paper, we introduce Paragraph Ranker which
ranks paragraphs of retrieved documents for a higher answer recall with less
noise. We show that ranking paragraphs and aggregating answers using Paragraph
Ranker improves performance of open-domain QA pipeline on the four open-domain
QA datasets by 7.8% on average.
| 2,018 | Computation and Language |
Detecting Satire in the News with Machine Learning | We built models with Logistic Regression and linear Support Vector Machines
on a large dataset consisting of regular news articles and news from satirical
websites, and showed that such linear classifiers on a corpus with about 60,000
articles can perform with a precision of 98.7% and a recall of 95.2% on a
random test set of the news. On the other hand, when testing the classifier on
"publication sources" which are completely unknown during training, only an
accuracy of 88.2% and an F1-score of 76.3% are achieved. As another result, we
showed that the same algorithm can distinguish between news written by the news
agency itself and paid articles from customers. Here the results had an
accuracy of 99%.
| 2,018 | Computation and Language |
Real Time Monitoring of Social Media and Digital Press | Talaia is a platform for monitoring social media and digital press. A
configurable crawler gathers content with respect to user defined domains or
topics. Crawled data is processed by means of the EliXa Sentiment Analysis
system. A Django powered interface provides data visualization for a user-based
analysis of the data. This paper presents the architecture of the system and
describes in detail its different components. To prove the validity of the
approach, two real use cases are accounted for: one in the cultural domain and
one in the political domain. Evaluation for the sentiment analysis task in both
scenarios is also provided, showing the capacity for domain adaptation.
| 2,019 | Computation and Language |
Attention-based Encoder-Decoder Networks for Spelling and Grammatical
Error Correction | Automatic spelling and grammatical correction systems are one of the most
widely used tools within natural language applications. In this thesis, we
assume the task of error correction as a type of monolingual machine
translation where the source sentence is potentially erroneous and the target
sentence should be the corrected form of the input. Our main focus in this
project is building neural network models for the task of error correction. In
particular, we investigate sequence-to-sequence and attention-based models
which have recently shown a higher performance than the state-of-the-art of
many language processing problems. We demonstrate that neural machine
translation models can be successfully applied to the task of error correction.
While the experiments of this research are performed on an Arabic corpus, our
methods in this thesis can be easily applied to any language.
| 2,018 | Computation and Language |
Hindi-English Code-Switching Speech Corpus | Code-switching refers to the usage of two languages within a sentence or
discourse. It is a global phenomenon among multilingual communities and has
emerged as an independent area of research. With the increasing demand for the
code-switching automatic speech recognition (ASR) systems, the development of a
code-switching speech corpus has become highly desirable. However, for training
such systems, very limited code-switched resources are available as yet. In
this work, we present our first efforts in building a code-switching ASR system
in the Indian context. For that purpose, we have created a Hindi-English
code-switching speech database. The database not only contains the speech
utterances with code-switching properties but also covers the session and the
speaker variations like pronunciation, accent, age, gender, etc. This database
can be applied in several speech signal processing applications, such as
code-switching ASR, language identification, language modeling, speech
synthesis etc. This paper mainly presents an analysis of the statistics of the
collected code-switching speech corpus. Later, the performance results for the
ASR task have been reported for the created database.
| 2,018 | Computation and Language |
Translating Navigation Instructions in Natural Language to a High-Level
Plan for Behavioral Robot Navigation | We propose an end-to-end deep learning model for translating free-form
natural language instructions to a high-level plan for behavioral robot
navigation. We use attention models to connect information from both the user
instructions and a topological representation of the environment. We evaluate
our model's performance on a new dataset containing 10,050 pairs of navigation
instructions. Our model significantly outperforms baseline approaches.
Furthermore, our results suggest that it is possible to leverage the
environment map as a relevant knowledge base to facilitate the translation of
free-form navigational instruction.
| 2,018 | Computation and Language |
Text Similarity in Vector Space Models: A Comparative Study | Automatic measurement of semantic text similarity is an important task in
natural language processing. In this paper, we evaluate the performance of
different vector space models to perform this task. We address the real-world
problem of modeling patent-to-patent similarity and compare TFIDF (and related
extensions), topic models (e.g., latent semantic indexing), and neural models
(e.g., paragraph vectors). Contrary to expectations, the added computational
cost of text embedding methods is justified only when: 1) the target text is
condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF
performs surprisingly well in other cases: in particular for longer and more
technical texts or for making finer-grained distinctions between nearest
neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun
phrases or calculating term weights incrementally, were not helpful in our
context.
| 2,018 | Computation and Language |
Wronging a Right: Generating Better Errors to Improve Grammatical Error
Detection | Grammatical error correction, like other machine learning tasks, greatly
benefits from large quantities of high quality training data, which is
typically expensive to produce. While writing a program to automatically
generate realistic grammatical errors would be difficult, one could learn the
distribution of naturallyoccurring errors and attempt to introduce them into
other datasets. Initial work on inducing errors in this way using statistical
machine translation has shown promise; we investigate cheaply constructing
synthetic samples, given a small corpus of human-annotated data, using an
off-the-rack attentive sequence-to-sequence model and a straight-forward
post-processing procedure. Our approach yields error-filled artificial data
that helps a vanilla bi-directional LSTM to outperform the previous state of
the art at grammatical error detection, and a previously introduced model to
gain further improvements of over 5% $F_{0.5}$ score. When attempting to
determine if a given sentence is synthetic, a human annotator at best achieves
39.39 $F_1$ score, indicating that our model generates mostly human-like
instances.
| 2,018 | Computation and Language |
Automatic Data Expansion for Customer-care Spoken Language Understanding | Spoken language understanding (SLU) systems are widely used in handling of
customer-care calls.A traditional SLU system consists of an acoustic model (AM)
and a language model (LM) that areused to decode the utterance and a natural
language understanding (NLU) model that predicts theintent. While AM can be
shared across different domains, LM and NLU models need to be
trainedspecifically for every new task. However, preparing enough data to train
these models is prohibitivelyexpensive. In this paper, we introduce an
efficient method to expand the limited in-domain data. Theprocess starts with
training a preliminary NLU model based on logistic regression on the
in-domaindata. Since the features are based onn= 1,2-grams, we can detect the
most informative n-gramsfor each intent class. Using these n-grams, we find the
samples in the out-of-domain corpus that1) contain the desired n-gram and/or 2)
have similar intent label. The ones which meet the firstconstraint are used to
train a new LM model and the ones that meet both constraints are used to train
anew NLU model. Our results on two divergent experimental setups show that the
proposed approachreduces by 30% the absolute classification error rate (CER)
comparing to the preliminary modelsand it significantly outperforms the
traditional data expansion algorithms such as the ones based onsemi-supervised
learning, TF-IDF and embedding vectors.
| 2,018 | Computation and Language |
NEXUS Network: Connecting the Preceding and the Following in Dialogue
Generation | Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in
building end-to-end trainable dialogue systems. Though highly efficient in
learning the backbone of human-computer communications, they suffer from the
problem of strongly favoring short generic responses. In this paper, we argue
that a good response should smoothly connect both the preceding dialogue
history and the following conversations. We strengthen this connection through
mutual information maximization. To sidestep the non-differentiability of
discrete natural language tokens, we introduce an auxiliary continuous code
space and map such code space to a learnable prior distribution for generation
purpose. Experiments on two dialogue datasets validate the effectiveness of our
model, where the generated responses are closely related to the dialogue
context and lead to more interactive conversations.
| 2,018 | Computation and Language |
Learning Robust, Transferable Sentence Representations for Text
Classification | Despite deep recurrent neural networks (RNNs) demonstrate strong performance
in text classification, training RNN models are often expensive and requires an
extensive collection of annotated data which may not be available. To overcome
the data limitation issue, existing approaches leverage either pre-trained word
embedding or sentence representation to lift the burden of training RNNs from
scratch. In this paper, we show that jointly learning sentence representations
from multiple text classification tasks and combining them with pre-trained
word-level and sentence level encoders result in robust sentence
representations that are useful for transfer learning. Extensive experiments
and analyses using a wide range of transfer and linguistic tasks endorse the
effectiveness of our approach.
| 2,018 | Computation and Language |
Joint On-line Learning of a Zero-shot Spoken Semantic Parser and a
Reinforcement Learning Dialogue Manager | Despite many recent advances for the design of dialogue systems, a true
bottleneck remains the acquisition of data required to train its components.
Unlike many other language processing applications, dialogue systems require
interactions with users, therefore it is complex to develop them with
pre-recorded data. Building on previous works, on-line learning is pursued here
as a most convenient way to address the issue. Data collection, annotation and
use in learning algorithms are performed in a single process. The main
difficulties are then: to bootstrap an initial basic system, and to control the
level of additional cost on the user side. Considering that well-performing
solutions can be used directly off the shelf for speech recognition and
synthesis, the study is focused on learning the spoken language understanding
and dialogue management modules only. Several variants of joint learning are
investigated and tested with user trials to confirm that the overall on-line
learning can be obtained after only a few hundred training dialogues and can
overstep an expert-based system.
| 2,018 | Computation and Language |
Challenges of Using Text Classifiers for Causal Inference | Causal understanding is essential for many kinds of decision-making, but
causal inference from observational data has typically only been applied to
structured, low-dimensional datasets. While text classifiers produce
low-dimensional outputs, their use in causal inference has not previously been
studied. To facilitate causal analyses based on language data, we consider the
role that text classifiers can play in causal inference through established
modeling mechanisms from the causality literature on missing data and
measurement error. We demonstrate how to conduct causal analyses using text
classifiers on simulated and Yelp data, and discuss the opportunities and
challenges of future work that uses text data in causal inference.
| 2,018 | Computation and Language |
Efficient and Accurate Abnormality Mining from Radiology Reports with
Customized False Positive Reduction | Obtaining datasets labeled to facilitate model development is a challenge for
most machine learning tasks. The difficulty is heightened for medical imaging,
where data itself is limited in accessibility and labeling requires costly time
and effort by trained medical specialists. Medical imaging studies, however,
are often accompanied by a medical report produced by a radiologist,
identifying important features on the corresponding scan for other physicians
not specifically trained in radiology. We propose a methodology for
approximating image-level labels for radiology studies from associated reports
using a general purpose language processing tool for medical concept extraction
and sentiment analysis, and simple manually crafted heuristics for false
positive reduction. Using this approach, we label more than 175,000 Head CT
studies for the presence of 33 features indicative of 11 clinically relevant
conditions. For 27 of the 30 keywords that yielded positive results (3 had no
occurrences), the lower bound of the confidence intervals created to estimate
the percentage of accurately labeled reports was above 85%, with the average
being above 95%. Though noisier then manual labeling, these results suggest
this method to be a viable means of labeling medical images at scale.
| 2,018 | Computation and Language |
Utilizing a Transparency-driven Environment toward Trusted Automatic
Genre Classification: A Case Study in Journalism History | With the growing abundance of unlabeled data in real-world tasks, researchers
have to rely on the predictions given by black-boxed computational models.
However, it is an often neglected fact that these models may be scoring high on
accuracy for the wrong reasons. In this paper, we present a practical impact
analysis of enabling model transparency by various presentation forms. For this
purpose, we developed an environment that empowers non-computer scientists to
become practicing data scientists in their own research field. We demonstrate
the gradually increasing understanding of journalism historians through a
real-world use case study on automatic genre classification of newspaper
articles. This study is a first step towards trusted usage of machine learning
pipelines in a responsible way.
| 2,018 | Computation and Language |
Improving Sentence Representations with Consensus Maximisation | Consensus maximisation learning can provide self-supervision when different
views are available of the same data. The distributional hypothesis provides
another form of useful self-supervision from adjacent sentences which are
plentiful in large unlabelled corpora. Motivated by the observation that
different learning architectures tend to emphasise different aspects of
sentence meaning, we present a new self-supervised learning framework for
learning sentence representations which minimises the disagreement between two
views of the same sentence where one view encodes the sentence with a recurrent
neural network (RNN), and the other view encodes the same sentence with a
simple linear model. After learning, the individual views (networks) result in
higher quality sentence representations than their single-view learnt
counterparts (learnt using only the distributional hypothesis) as judged by
performance on standard downstream tasks. An ensemble of both views provides
even better generalisation on both supervised and unsupervised downstream
tasks. Also, importantly the ensemble of views trained with consensus
maximisation between the two different architectures performs better on
downstream tasks than an analogous ensemble made from the single-view trained
counterparts.
| 2,019 | Computation and Language |
Semi-supervised Text Regression with Conditional Generative Adversarial
Networks | Enormous online textual information provides intriguing opportunities for
understandings of social and economic semantics. In this paper, we propose a
novel text regression model based on a conditional generative adversarial
network (GAN), with an attempt to associate textual data and social outcomes in
a semi-supervised manner. Besides promising potential of predicting
capabilities, our superiorities are twofold: (i) the model works with
unbalanced datasets of limited labelled data, which align with real-world
scenarios; and (ii) predictions are obtained by an end-to-end framework,
without explicitly selecting high-level representations. Finally we point out
related datasets for experiments and future research directions.
| 2,019 | Computation and Language |
Findings of the E2E NLG Challenge | This paper summarises the experimental setup and results of the first shared
task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue
systems. Recent end-to-end generation systems are promising since they reduce
the need for data annotation. However, they are currently limited to small,
delexicalised datasets. The E2E NLG shared task aims to assess whether these
novel approaches can generate better-quality output by learning from a dataset
containing higher lexical richness, syntactic complexity and diverse discourse
phenomena. We compare 62 systems submitted by 17 institutions, covering a wide
range of approaches, including machine learning architectures -- with the
majority implementing sequence-to-sequence models (seq2seq) -- as well as
systems based on grammatical rules and templates.
| 2,018 | Computation and Language |
A Knowledge Hunting Framework for Common Sense Reasoning | We introduce an automatic system that achieves state-of-the-art results on
the Winograd Schema Challenge (WSC), a common sense reasoning task that
requires diverse, complex forms of inference and knowledge. Our method uses a
knowledge hunting module to gather text from the web, which serves as evidence
for candidate problem resolutions. Given an input problem, our system generates
relevant queries to send to a search engine, then extracts and classifies
knowledge from the returned results and weighs them to make a resolution. Our
approach improves F1 performance on the full WSC by 0.21 over the previous best
and represents the first system to exceed 0.5 F1. We further demonstrate that
the approach is competitive on the Choice of Plausible Alternatives (COPA)
task, which suggests that it is generally applicable.
| 2,018 | Computation and Language |
Learning to Segment Inputs for NMT Favors Character-Level Processing | Most modern neural machine translation (NMT) systems rely on presegmented
inputs. Segmentation granularity importantly determines the input and output
sequence lengths, hence the modeling depth, and source and target vocabularies,
which in turn determine model size, computational costs of softmax
normalization, and handling of out-of-vocabulary words. However, the current
practice is to use static, heuristic-based segmentations that are fixed before
NMT training. This begs the question whether the chosen segmentation is optimal
for the translation task. To overcome suboptimal segmentation choices, we
present an algorithm for dynamic segmentation based on the Adaptative
Computation Time algorithm (Graves 2016), that is trainable end-to-end and
driven by the NMT objective. In an evaluation on four translation tasks we
found that, given the freedom to navigate between different segmentation
levels, the model prefers to operate on (almost) character level, providing
support for purely character-level NMT models from a novel angle.
| 2,018 | Computation and Language |
A Deep Learning Architecture for De-identification of Patient Notes:
Implementation and Evaluation | De-identification is the process of removing 18 protected health information
(PHI) from clinical notes in order for the text to be considered not
individually identifiable. Recent advances in natural language processing (NLP)
has allowed for the use of deep learning techniques for the task of
de-identification. In this paper, we present a deep learning architecture that
builds on the latest NLP advances by incorporating deep contextualized word
embeddings and variational drop out Bi-LSTMs. We test this architecture on two
gold standard datasets and show that the architecture achieves state-of-the-art
performance on both data sets while also converging faster than other systems
without the use of dictionaries or other knowledge sources.
| 2,018 | Computation and Language |
A Comparative Study of Neural Network Models for Sentence Classification | This paper presents an extensive comparative study of four neural network
models, including feed-forward networks, convolutional networks, recurrent
networks and long short-term memory networks, on two sentence classification
datasets of English and Vietnamese text. We show that on the English dataset,
the convolutional network models without any feature engineering outperform
some competitive sentence classifiers with rich hand-crafted linguistic
features. We demonstrate that the GloVe word embeddings are consistently better
than both Skip-gram word embeddings and word count vectors. We also show the
superiority of convolutional neural network models on a Vietnamese newspaper
sentence dataset over strong baseline models. Our experimental results suggest
some good practices for applying neural network models in sentence
classification.
| 2,018 | Computation and Language |
A Neural Transition-based Model for Nested Mention Recognition | It is common that entity mentions can contain other mentions recursively.
This paper introduces a scalable transition-based method to model the nested
structure of mentions. We first map a sentence with nested mentions to a
designated forest where each mention corresponds to a constituent of the
forest. Our shift-reduce based system then learns to construct the forest
structure in a bottom-up manner through an action sequence whose maximal length
is guaranteed to be three times of the sentence length. Based on Stack-LSTM
which is employed to efficiently and effectively represent the states of the
system in a continuous space, our system is further incorporated with a
character-based component to capture letter-level patterns. Our model achieves
the state-of-the-art results on ACE datasets, showing its effectiveness in
detecting nested mentions.
| 2,018 | Computation and Language |
Neural Segmental Hypergraphs for Overlapping Mention Recognition | In this work, we propose a novel segmental hypergraph representation to model
overlapping entity mentions that are prevalent in many practical datasets. We
show that our model built on top of such a new representation is able to
capture features and interactions that cannot be captured by previous models
while maintaining a low time complexity for inference. We also present a
theoretical analysis to formally assess how our representation is better than
alternative representations reported in the literature in terms of
representational power. Coupled with neural networks for feature learning, our
model achieves the state-of-the-art performance in three benchmark datasets
annotated with overlapping mentions.
| 2,018 | Computation and Language |
Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain
using Transfer Learning | This study proposes a novel way of identifying the sentiment of the phrases
used in the legal domain. The added complexity of the language used in law, and
the inability of the existing systems to accurately predict the sentiments of
words in law are the main motivations behind this study. This is a transfer
learning approach, which can be used for other domain adaptation tasks as well.
The proposed methodology achieves an improvement of over 6\% compared to the
source model's accuracy in the legal domain.
| 2,018 | Computation and Language |
Semi-Supervised Methods for Out-of-Domain Dependency Parsing | Dependency parsing is one of the important natural language processing tasks
that assigns syntactic trees to texts. Due to the wider availability of
dependency corpora and improved parsing and machine learning techniques,
parsing accuracies of supervised learning-based systems have been significantly
improved. However, due to the nature of supervised learning, those parsing
systems highly rely on the manually annotated training corpora. They work
reasonably good on the in-domain data but the performance drops significantly
when tested on out-of-domain texts. To bridge the performance gap between
in-domain and out-of-domain, this thesis investigates three semi-supervised
techniques for out-of-domain dependency parsing, namely co-training,
self-training and dependency language models. Our approaches use easily
obtainable unlabelled data to improve out-of-domain parsing accuracies without
the need of expensive corpora annotation. The evaluations on several English
domains and multi-lingual data show quite good improvements on parsing
accuracy. Overall this work conducted a survey of semi-supervised methods for
out-of-domain dependency parsing, where I extended and compared a number of
important semi-supervised methods in a unified framework. The comparison
between those techniques shows that self-training works equally well as
co-training on out-of-domain parsing, while dependency language models can
improve both in- and out-of-domain accuracies.
| 2,018 | Computation and Language |
Zooming Network | Structural information is important in natural language understanding.
Although some current neural net-based models have a limited ability to take
local syntactic information, they fail to use high-level and large-scale
structures of documents. This information is valuable for text understanding
since it contains the author's strategy to express information, in building an
effective representation and forming appropriate output modes. We propose a
neural net-based model, Zooming Network, capable of representing and leveraging
text structure of long document and developing its own analyzing rhythm to
extract critical information. Generally, ZN consists of an encoding neural net
that can build a hierarchical representation of a document, and an interpreting
neural model that can read the information at multi-levels and issuing labeling
actions through a policy-net. Our model is trained with a hybrid paradigm of
supervised learning (distinguishing right and wrong decision) and reinforcement
learning (determining the goodness among multiple right paths). We applied the
proposed model to long text sequence labeling tasks, with performance exceeding
baseline model (biLSTM-crf) by 10 F1-measure.
| 2,018 | Computation and Language |
Neural Networks for Cross-lingual Negation Scope Detection | Negation scope has been annotated in several English and Chinese corpora, and
highly accurate models for this task in these languages have been learned from
these annotations. Unfortunately, annotations are not available in other
languages. Could a model that detects negation scope be applied to a language
that it hasn't been trained on? We develop neural models that learn from
cross-lingual word embeddings or universal dependencies in English, and test
them on Chinese, showing that they work surprisingly well. We find that
modelling syntax is helpful even in monolingual settings and that cross-lingual
word embeddings help relatively little, and we analyse cases that are still
difficult for this task.
| 2,018 | Computation and Language |
Italian Event Detection Goes Deep Learning | This paper reports on a set of experiments with different word embeddings to
initialize a state-of-the-art Bi-LSTM-CRF network for event detection and
classification in Italian, following the EVENTI evaluation exercise. The net-
work obtains a new state-of-the-art result by improving the F1 score for
detection of 1.3 points, and of 6.5 points for classification, by using a
single step approach. The results also provide further evidence that embeddings
have a major impact on the performance of such architectures.
| 2,018 | Computation and Language |
A Span Selection Model for Semantic Role Labeling | We present a simple and accurate span-based model for semantic role labeling
(SRL). Our model directly takes into account all possible argument spans and
scores them for each label. At decoding time, we greedily select higher scoring
labeled spans. One advantage of our model is to allow us to design and use
span-level features, that are difficult to use in token-based BIO tagging
approaches. Experimental results demonstrate that our ensemble model achieves
the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012
datasets, respectively.
| 2,018 | Computation and Language |
A Large-Scale Test Set for the Evaluation of Context-Aware Pronoun
Translation in Neural Machine Translation | The translation of pronouns presents a special challenge to machine
translation to this day, since it often requires context outside the current
sentence. Recent work on models that have access to information across sentence
boundaries has seen only moderate improvements in terms of automatic evaluation
metrics such as BLEU. However, metrics that quantify the overall translation
quality are ill-equipped to measure gains from additional context. We argue
that a different kind of evaluation is needed to assess how well models
translate inter-sentential phenomena such as pronouns. This paper therefore
presents a test suite of contrastive translations focused specifically on the
translation of pronouns. Furthermore, we perform experiments with several
context-aware models. We show that, while gains in BLEU are moderate for those
systems, they outperform baselines by a large margin in terms of accuracy on
our contrastive test set. Our experiments also show the effectiveness of
parameter tying for multi-encoder architectures.
| 2,019 | Computation and Language |
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in
Conversations | Emotion recognition in conversations is a challenging task that has recently
gained popularity due to its potential applications. Until now, however, a
large-scale multimodal multi-party emotional conversational database containing
more than two speakers per dialogue was missing. Thus, we propose the
Multimodal EmotionLines Dataset (MELD), an extension and enhancement of
EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from
the TV-series Friends. Each utterance is annotated with emotion and sentiment
labels, and encompasses audio, visual and textual modalities. We propose
several strong multimodal baselines and show the importance of contextual and
multimodal information for emotion recognition in conversations. The full
dataset is available for use at http:// affective-meld.github.io.
| 2,019 | Computation and Language |
Integrating Weakly Supervised Word Sense Disambiguation into Neural
Machine Translation | This paper demonstrates that word sense disambiguation (WSD) can improve
neural machine translation (NMT) by widening the source context considered when
modeling the senses of potentially ambiguous words. We first introduce three
adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant
processes, and random walks, which are then applied to large word contexts
represented in a low-rank space and evaluated on SemEval shared-task data. We
then learn word vectors jointly with sense vectors defined by our best WSD
method, within a state-of-the-art NMT system. We show that the concatenation of
these vectors, and the use of a sense selection mechanism based on the weighted
average of sense vectors, outperforms several baselines including sense-aware
ones. This is demonstrated by translation on five language pairs. The
improvements are above one BLEU point over strong NMT baselines, +4% accuracy
over all ambiguous nouns and verbs, or +20% when scored manually over several
challenging words.
| 2,018 | Computation and Language |
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic
Parsing and Code Generation | We present TRANX, a transition-based neural semantic parser that maps natural
language (NL) utterances into formal meaning representations (MRs). TRANX uses
a transition system based on the abstract syntax description language for the
target MR, which gives it two major advantages: (1) it is highly accurate,
using information from the syntax of the target MR to constrain the output
space and model the information flow, and (2) it is highly generalizable, and
can easily be applied to new types of MR by just writing a new abstract syntax
description corresponding to the allowable structures in the MR. Experiments on
four different semantic parsing and code generation tasks show that our system
is generalizable, extensible, and effective, registering strong results
compared to existing neural semantic parsers.
| 2,018 | Computation and Language |
From direct tagging to Tagging with sentences compression | In essence, the two tagging methods (direct tagging and tagging with
sentences compression) are to tag the information we need by using regular
expression which basing on the inherent language patterns of the natural
language. Though it has many advantages in extracting regular data, Direct
tagging is not applicable to some situations. if the data we need extract is
not regular and its surrounding words are regular is relatively regular, then
we can use information compression to cut the information we do not need before
we tagging the data we need. In this way we can increase the precision of the
data while not undermine the recall of the data.
| 2,018 | Computation and Language |
Learning to Encode Text as Human-Readable Summaries using Generative
Adversarial Networks | Auto-encoders compress input data into a latent-space representation and
reconstruct the original data from the representation. This latent
representation is not easily interpreted by humans. In this paper, we propose
training an auto-encoder that encodes input text into human-readable sentences,
and unpaired abstractive summarization is thereby achieved. The auto-encoder is
composed of a generator and a reconstructor. The generator encodes the input
text into a shorter word sequence, and the reconstructor recovers the generator
input from the generator output. To make the generator output human-readable, a
discriminator restricts the output of the generator to resemble human-written
sentences. By taking the generator output as the summary of the input text,
abstractive summarization is achieved without document-summary pairs as
training data. Promising results are shown on both English and Chinese corpora.
| 2,018 | Computation and Language |
Scalable Micro-planned Generation of Discourse from Structured Data | We present a framework for generating natural language description from
structured data such as tables; the problem comes under the category of
data-to-text natural language generation (NLG). Modern data-to-text NLG systems
typically employ end-to-end statistical and neural architectures that learn
from a limited amount of task-specific labeled data, and therefore, exhibit
limited scalability, domain-adaptability, and interpretability. Unlike these
systems, ours is a modular, pipeline-based approach, and does not require
task-specific parallel data. It rather relies on monolingual corpora and basic
off-the-shelf NLP tools. This makes our system more scalable and easily
adaptable to newer domains.
Our system employs a 3-staged pipeline that: (i) converts entries in the
structured data to canonical form, (ii) generates simple sentences for each
atomic entry in the canonicalized representation, and (iii) combines the
sentences to produce a coherent, fluent and adequate paragraph description
through sentence compounding and co-reference replacement modules. Experiments
on a benchmark mixed-domain dataset curated for paragraph description from
tables reveals the superiority of our system over existing data-to-text
approaches. We also demonstrate the robustness of our system in accepting other
popular datasets covering diverse data types such as Knowledge Graphs and
Key-Value maps.
| 2,019 | Computation and Language |
Entity Tracking Improves Cloze-style Reading Comprehension | Reading comprehension tasks test the ability of models to process long-term
context and remember salient information. Recent work has shown that relatively
simple neural methods such as the Attention Sum-Reader can perform well on
these tasks; however, these systems still significantly trail human
performance. Analysis suggests that many of the remaining hard instances are
related to the inability to track entity-references throughout documents. This
work focuses on these hard entity tracking cases with two extensions: (1)
additional entity features, and (2) training with a multi-task tracking
objective. We show that these simple modifications improve performance both
independently and in combination, and we outperform the previous state of the
art on the LAMBADA dataset, particularly on difficult entity examples.
| 2,018 | Computation and Language |
Co-Stack Residual Affinity Networks with Multi-level Attention
Refinement for Matching Text Sequences | Learning a matching function between two text sequences is a long standing
problem in NLP research. This task enables many potential applications such as
question answering and paraphrase identification. This paper proposes Co-Stack
Residual Affinity Networks (CSRAN), a new and universal neural architecture for
this problem. CSRAN is a deep architecture, involving stacked (multi-layered)
recurrent encoders. Stacked/Deep architectures are traditionally difficult to
train, due to the inherent weaknesses such as difficulty with feature
propagation and vanishing gradients. CSRAN incorporates two novel components to
take advantage of the stacked architecture. Firstly, it introduces a new
bidirectional alignment mechanism that learns affinity weights by fusing
sequence pairs across stacked hierarchies. Secondly, it leverages a multi-level
attention refinement component between stacked recurrent layers. The key
intuition is that, by leveraging information across all network hierarchies, we
can not only improve gradient flow but also improve overall performance. We
conduct extensive experiments on six well-studied text sequence matching
datasets, achieving state-of-the-art performance on all.
| 2,018 | Computation and Language |
Personality facets recognition from text | Fundamental Big Five personality traits (e.g., Extraversion) and their facets
(e.g., Activity) are known to correlate with a broad range of linguistic
features and, accordingly, the recognition of personality traits from text is a
well-known Natural Language Processing task. Labelling text data with facets
information, however, may require the use of lengthy personality inventories,
and perhaps for that reason existing computational models of this kind are
usually limited to the recognition of the fundamental traits. Based on these
observations, this paper investigates the issue of personality facets
recognition from text labelled only with information available from a shorter
personality inventory. In doing so, we provide a low-cost model for the
recognition of certain personality facets, and present reference results for
further studies in this field.
| 2,019 | Computation and Language |
Text-based Sentiment Analysis and Music Emotion Recognition | Sentiment polarity of tweets, blog posts or product reviews has become highly
attractive and is utilized in recommender systems, market predictions, business
intelligence and more. Deep learning techniques are becoming top performers on
analyzing such texts. There are however several problems that need to be solved
for efficient use of deep neural networks on text mining and text polarity
analysis. First, deep neural networks need to be fed with data sets that are
big in size as well as properly labeled. Second, there are various
uncertainties regarding the use of word embedding vectors: should they be
generated from the same data set that is used to train the model or it is
better to source them from big and popular collections? Third, to simplify
model creation it is convenient to have generic neural network architectures
that are effective and can adapt to various texts, encapsulating much of design
complexity. This thesis addresses the above problems to provide methodological
and practical insights for utilizing neural networks on sentiment analysis of
texts and achieving state of the art results. Regarding the first problem, the
effectiveness of various crowdsourcing alternatives is explored and two
medium-sized and emotion-labeled song data sets are created utilizing social
tags. To address the second problem, a series of experiments with large text
collections of various contents and domains were conducted, trying word
embeddings of various parameters. Regarding the third problem, a series of
experiments involving convolution and max-pooling neural layers were conducted.
Combining convolutions of words, bigrams, and trigrams with regional
max-pooling layers in a couple of stacks produced the best results. The derived
architecture achieves competitive performance on sentiment polarity analysis of
movie, business and product reviews.
| 2,020 | Computation and Language |
Assessing Crosslingual Discourse Relations in Machine Translation | In an attempt to improve overall translation quality, there has been an
increasing focus on integrating more linguistic elements into Machine
Translation (MT). While significant progress has been achieved, especially
recently with neural models, automatically evaluating the output of such
systems is still an open problem. Current practice in MT evaluation relies on a
single reference translation, even though there are many ways of translating a
particular text, and it tends to disregard higher level information such as
discourse. We propose a novel approach that assesses the translated output
based on the source text rather than the reference translation, and measures
the extent to which the semantics of the discourse elements (discourse
relations, in particular) in the source text are preserved in the MT output.
The challenge is to detect the discourse relations in the source text and
determine whether these relations are correctly transferred crosslingually to
the target language -- without a reference translation. This methodology could
be used independently for discourse-level evaluation, or as a component in
other metrics, at a time where substantial amounts of MT are online and would
benefit from evaluation where the source text serves as a benchmark.
| 2,018 | Computation and Language |
Unsupervised Neural Word Segmentation for Chinese via Segmental Language
Modeling | Previous traditional approaches to unsupervised Chinese word segmentation
(CWS) can be roughly classified into discriminative and generative models. The
former uses the carefully designed goodness measures for candidate
segmentation, while the latter focuses on finding the optimal segmentation of
the highest generative probability. However, while there exists a trivial way
to extend the discriminative models into neural version by using neural
language models, those of generative ones are non-trivial. In this paper, we
propose the segmental language models (SLMs) for CWS. Our approach explicitly
focuses on the segmental nature of Chinese, as well as preserves several
properties of language models. In SLMs, a context encoder encodes the previous
context and a segment decoder generates each segment incrementally. As far as
we know, we are the first to propose a neural model for unsupervised CWS and
achieve competitive performance to the state-of-the-art statistical models on
four different datasets from SIGHAN 2005 bakeoff.
| 2,018 | Computation and Language |
Phonology-Augmented Statistical Framework for Machine Transliteration
using Limited Linguistic Resources | Transliteration converts words in a source language (e.g., English) into
words in a target language (e.g., Vietnamese). This conversion considers the
phonological structure of the target language, as the transliterated output
needs to be pronounceable in the target language. For example, a word in
Vietnamese that begins with a consonant cluster is phonologically invalid and
thus would be an incorrect output of a transliteration system. Most statistical
transliteration approaches, albeit being widely adopted, do not explicitly
model the target language's phonology, which often results in invalid outputs.
The problem is compounded by the limited linguistic resources available when
converting foreign words to transliterated words in the target language. In
this work, we present a phonology-augmented statistical framework suitable for
transliteration, especially when only limited linguistic resources are
available. We propose the concept of pseudo-syllables as structures
representing how segments of a foreign word are organized according to the
syllables of the target language's phonology. We performed transliteration
experiments on Vietnamese and Cantonese. We show that the proposed framework
outperforms the statistical baseline by up to 44.68% relative, when there are
limited training examples (587 entries).
| 2,019 | Computation and Language |
Query Tracking for E-commerce Conversational Search: A Machine
Comprehension Perspective | With the development of dialog techniques, conversational search has
attracted more and more attention as it enables users to interact with the
search engine in a natural and efficient manner. However, comparing with the
natural language understanding in traditional task-oriented dialog which
focuses on slot filling and tracking, the query understanding in E-commerce
conversational search is quite different and more challenging due to more
diverse user expressions and complex intentions. In this work, we define the
real-world problem of query tracking in E-commerce conversational search, in
which the goal is to update the internal query after each round of interaction.
We also propose a self attention based neural network to handle the task in a
machine comprehension perspective. Further more we build a novel E-commerce
query tracking dataset from an operational E-commerce Search Engine, and
experimental results on this dataset suggest that our proposed model
outperforms several baseline methods by a substantial gain for Exact Match
accuracy and F1 score, showing the potential of machine comprehension like
model for this task.
| 2,018 | Computation and Language |
Multi-Task Learning for Domain-General Spoken Disfluency Detection in
Dialogue Systems | Spontaneous spoken dialogue is often disfluent, containing pauses,
hesitations, self-corrections and false starts. Processing such phenomena is
essential in understanding a speaker's intended meaning and controlling the
flow of the conversation. Furthermore, this processing needs to be word-by-word
incremental to allow further downstream processing to begin as early as
possible in order to handle real spontaneous human conversational behaviour.
In addition, from a developer's point of view, it is highly desirable to be
able to develop systems which can be trained from `clean' examples while also
able to generalise to the very diverse disfluent variations on the same data --
thereby enhancing both data-efficiency and robustness. In this paper, we
present a multi-task LSTM-based model for incremental detection of disfluency
structure, which can be hooked up to any component for incremental
interpretation (e.g. an incremental semantic parser), or else simply used to
`clean up' the current utterance as it is being produced.
We train the system on the Switchboard Dialogue Acts (SWDA) corpus and
present its accuracy on this dataset. Our model outperforms prior neural
network-based incremental approaches by about 10 percentage points on SWDA
while employing a simpler architecture. To test the model's generalisation
potential, we evaluate the same model on the bAbI+ dataset, without any
additional training. bAbI+ is a dataset of synthesised goal-oriented dialogues
where we control the distribution of disfluencies and their types. This shows
that our approach has good generalisation potential, and sheds more light on
which types of disfluency might be amenable to domain-general processing.
| 2,018 | Computation and Language |
Phrase-Based Attentions | Most state-of-the-art neural machine translation systems, despite being
different in architectural skeletons (e.g. recurrence, convolutional), share an
indispensable feature: the Attention. However, most existing attention methods
are token-based and ignore the importance of phrasal alignments, the key
ingredient for the success of phrase-based statistical machine translation. In
this paper, we propose novel phrase-based attention methods to model n-grams of
tokens as attention entities. We incorporate our phrase-based attentions into
the recently proposed Transformer network, and demonstrate that our approach
yields improvements of 1.3 BLEU for English-to-German and 0.5 BLEU for
German-to-English translation tasks on WMT newstest2014 using WMT'16 training
data.
| 2,019 | Computation and Language |
Building a language evolution tree based on word vector combination
model | In this paper, we try to explore the evolution of language through case
calculations. First, we chose the novels of eleven British writers from 1400 to
2005 and found the corresponding works; Then, we use the natural language
processing tool to construct the corresponding eleven corpora, and calculate
the respective word vectors of 100 high-frequency words in eleven corpora;
Next, for each corpus, we concatenate the 100 word vectors from beginning to
end into one; Finally, we use the similarity comparison and hierarchical
clustering method to generate the relationship tree between the combined eleven
word vectors. This tree represents the relationship between eleven corpora. We
found that in the tree generated by clustering, the distance between the corpus
and the year corresponding to the corpus are basically the same. This means
that we have discovered a specific language evolution tree. To verify the
stability and versatility of this method, we add three other themes: Dickens's
eight works, the 19th century poets' works, and art criticism of recent 60
years. For these four themes, we tested different parameters such as the time
span of the corpus, the time interval between the corpora, the dimension of the
word vector, and the number of high-frequency public words. The results show
that this is fairly stable and versatile.
| 2,018 | Computation and Language |
Exploiting Contextual Information via Dynamic Memory Network for Event
Detection | The task of event detection involves identifying and categorizing event
triggers. Contextual information has been shown effective on the task. However,
existing methods which utilize contextual information only process the context
once. We argue that the context can be better exploited by processing the
context multiple times, allowing the model to perform complex reasoning and to
generate better context representation, thus improving the overall performance.
Meanwhile, dynamic memory network (DMN) has demonstrated promising capability
in capturing contextual information and has been applied successfully to
various tasks. In light of the multi-hop mechanism of the DMN to model the
context, we propose the trigger detection dynamic memory network (TD-DMN) to
tackle the event detection problem. We performed a five-fold cross-validation
on the ACE-2005 dataset and experimental results show that the multi-hop
mechanism does improve the performance and the proposed model achieves best
$F_1$ score compared to the state-of-the-art methods.
| 2,018 | Computation and Language |
Active Learning for New Domains in Natural Language Understanding | We explore active learning (AL) for improving the accuracy of new domains in
a natural language understanding (NLU) system. We propose an algorithm called
Majority-CRF that uses an ensemble of classification models to guide the
selection of relevant utterances, as well as a sequence labeling model to help
prioritize informative examples. Experiments with three domains show that
Majority-CRF achieves 6.6%-9% relative error rate reduction compared to random
sampling with the same annotation budget, and statistically significant
improvements compared to other AL approaches. Additionally, case studies with
human-in-the-loop AL on six new domains show 4.6%-9% improvement on an existing
NLU system.
| 2,019 | Computation and Language |
Multilingual sequence-to-sequence speech recognition: architecture,
transfer learning, and language modeling | Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively
new direction in speech research. The approach benefits by performing model
training without using lexicon and alignments. However, this poses a new
problem of requiring more data compared to conventional DNN-HMM systems. In
this work, we attempt to use data from 10 BABEL languages to build a
multi-lingual seq2seq model as a prior model, and then port them towards 4
other BABEL languages using transfer learning approach. We also explore
different architectures for improving the prior multilingual seq2seq model. The
paper also discusses the effect of integrating a recurrent neural network
language model (RNNLM) with a seq2seq model during decoding. Experimental
results show that the transfer learning approach from the multilingual model
shows substantial gains over monolingual models across all 4 BABEL languages.
Incorporating an RNNLM also brings significant improvements in terms of %WER,
and achieves recognition performance comparable to the models trained with
twice more training data.
| 2,018 | Computation and Language |
Sentence Segmentation for Classical Chinese Based on LSTM with Radical
Embedding | In this paper, we develop a low than character feature embedding called
radical embedding, and apply it on LSTM model for sentence segmentation of pre
modern Chinese texts. The datasets includes over 150 classical Chinese books
from 3 different dynasties and contains different literary styles. LSTM CRF
model is a state of art method for the sequence labeling problem. Our new model
adds a component of radical embedding, which leads to improved performances.
Experimental results based on the aforementioned Chinese books demonstrates a
better accuracy than earlier methods on sentence segmentation, especial in Tang
Epitaph texts.
| 2,020 | Computation and Language |
Text Classification of the Precursory Accelerating Seismicity Corpus:
Inference on some Theoretical Trends in Earthquake Predictability Research
from 1988 to 2018 | Text analytics based on supervised machine learning classifiers has shown
great promise in a multitude of domains, but has yet to be applied to
Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors,
Support Vector Machines, and Random Forests) on a seismological corpus of 100
articles related to the topic of precursory accelerating seismicity, spanning
from 1988 to 2010. This corpus was labelled in Mignan (2011) with the precursor
whether explained by critical processes (i.e., cascade triggering) or by other
processes (such as signature of main fault loading). We investigate rather the
classification process can be automatized to help analyze larger corpora in
order to better understand trends in earthquake predictability research. We
find that the Naive Bayes model performs best, in agreement with the machine
learning literature for the case of small datasets, with cross-validation
accuracies of 86% for binary classification. For a refined multiclass
classification ('non-critical process' < 'agnostic' < 'critical process
assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy.
Prediction on a dozen of articles published since 2011 shows however a weak
generalization with a F1-score of 60%, only slightly better than a random
classifier, which can be explained by a change of authorship and use of
different terminologies. Yet, the model shows F1-scores greater than 80% for
the two multiclass extremes ('non-critical process' versus 'critical process
demonstrated') while it falls to random classifier results (around 25%) for
papers labelled 'agnostic' or 'critical process assumed'. Those results are
encouraging in view of the small size of the corpus and of the high degree of
abstraction of the labelling. Domain knowledge engineering remains essential
but can be made transparent by an investigation of Naive Bayes keyword
posterior probabilities.
| 2,019 | Computation and Language |
An AMR Aligner Tuned by Transition-based Parser | In this paper, we propose a new rich resource enhanced AMR aligner which
produces multiple alignments and a new transition system for AMR parsing along
with its oracle parser. Our aligner is further tuned by our oracle parser via
picking the alignment that leads to the highest-scored achievable AMR graph.
Experimental results show that our aligner outperforms the rule-based aligner
in previous work by achieving higher alignment F1 score and consistently
improving two open-sourced AMR parsers. Based on our aligner and transition
system, we develop a transition-based AMR parser that parses a sentence into
its AMR graph directly. An ensemble of our parsers with only words and POS tags
as input leads to 68.4 Smatch F1 score.
| 2,018 | Computation and Language |
Multi-Source Cross-Lingual Model Transfer: Learning What to Share | Modern NLP applications have enjoyed a great boost utilizing neural networks
models. Such deep neural models, however, are not applicable to most human
languages due to the lack of annotated training data for various NLP tasks.
Cross-lingual transfer learning (CLTL) is a viable method for building NLP
models for a low-resource target language by leveraging labeled data from other
(source) languages. In this work, we focus on the multilingual transfer setting
where training data in multiple source languages is leveraged to further boost
target language performance.
Unlike most existing methods that rely only on language-invariant features
for CLTL, our approach coherently utilizes both language-invariant and
language-specific features at instance level. Our model leverages adversarial
networks to learn language-invariant features, and mixture-of-experts models to
dynamically exploit the similarity between the target language and each
individual source language. This enables our model to learn effectively what to
share between various languages in the multilingual setup. Moreover, when
coupled with unsupervised multilingual embeddings, our model can operate in a
zero-resource setting where neither target language training data nor
cross-lingual resources are available. Our model achieves significant
performance gains over prior art, as shown in an extensive set of experiments
over multiple text classification and sequence tagging tasks including a
large-scale industry dataset.
| 2,019 | Computation and Language |
Improving the Transformer Translation Model with Document-Level Context | Although the Transformer translation model (Vaswani et al., 2017) has
achieved state-of-the-art performance in a variety of translation tasks, how to
use document-level context to deal with discourse phenomena problematic for
Transformer still remains a challenge. In this work, we extend the Transformer
model with a new context encoder to represent document-level context, which is
then incorporated into the original encoder and decoder. As large-scale
document-level parallel corpora are usually not available, we introduce a
two-step training method to take full advantage of abundant sentence-level
parallel corpora and limited document-level parallel corpora. Experiments on
the NIST Chinese-English datasets and the IWSLT French-English datasets show
that our approach improves over Transformer significantly.
| 2,018 | Computation and Language |
End-to-End Text Classification via Image-based Embedding using
Character-level Networks | For analysing and/or understanding languages having no word boundaries based
on morphological analysis such as Japanese, Chinese, and Thai, it is desirable
to perform appropriate word segmentation before word embeddings. But it is
inherently difficult in these languages. In recent years, various language
models based on deep learning have made remarkable progress, and some of these
methodologies utilizing character-level features have successfully avoided such
a difficult problem. However, when a model is fed character-level features of
the above languages, it often causes overfitting due to a large number of
character types. In this paper, we propose a CE-CLCNN, character-level
convolutional neural networks using a character encoder to tackle these
problems. The proposed CE-CLCNN is an end-to-end learning model and has an
image-based character encoder, i.e. the CE-CLCNN handles each character in the
target document as an image. Through various experiments, we found and
confirmed that our CE-CLCNN captured closely embedded features for visually and
semantically similar characters and achieves state-of-the-art results on
several open document classification tasks. In this paper we report the
performance of our CE-CLCNN with the Wikipedia title estimation task and
analyse the internal behaviour.
| 2,019 | Computation and Language |
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet
Powerful Techniques | Several lexica for sentiment analysis have been developed and made available
in the NLP community. While most of these come with word polarity annotations
(e.g. positive/negative), attempts at building lexica for finer-grained emotion
analysis (e.g. happiness, sadness) have recently attracted significant
attention. Such lexica are often exploited as a building block in the process
of developing learning models for which emotion recognition is needed, and/or
used as baselines to which compare the performance of the models. In this work,
we contribute two new resources to the community: a) an extension of an
existing and widely used emotion lexicon for English; and b) a novel version of
the lexicon targeting Italian. Furthermore, we show how simple techniques can
be used, both in supervised and unsupervised experimental settings, to boost
performances on datasets and tasks of varying degree of domain-specificity.
| 2,018 | Computation and Language |
Comparing Models of Associative Meaning: An Empirical Investigation of
Reference in Simple Language Games | Simple reference games are of central theoretical and empirical importance in
the study of situated language use. Although language provides rich,
compositional truth-conditional semantics to facilitate reference, speakers and
listeners may sometimes lack the overall lexical and cognitive resources to
guarantee successful reference through these means alone. However, language
also has rich associational structures that can serve as a further resource for
achieving successful reference. Here we investigate this use of associational
information in a setting where only associational information is available: a
simplified version of the popular game Codenames. Using optimal experiment
design techniques, we compare a range of models varying in the type of
associative information deployed and in level of pragmatic sophistication
against human behavior. In this setting, we find that listeners' behavior
reflects direct bigram collocational associations more strongly than
word-embedding or semantic knowledge graph-based associations and that there is
little evidence for pragmatically sophisticated behavior by either speakers or
listeners of the type that might be predicted by recursive-reasoning models
such as the Rational Speech Acts theory. These results shed light on the nature
of the lexical resources that speakers and listeners can bring to bear in
achieving reference through associative meaning alone.
| 2,018 | Computation and Language |
Towards Verifying Semantic Roles Co-occurrence | Semantic role theory considers roles as a small universal set of unanalyzed
entities. It means that formally there are no restrictions on role
combinations. We argue that the semantic roles co-occur in verb
representations. It means that there are hidden restrictions on role
combinations. To demonstrate that a practical and evidence-based approach has
been built on in-depth analysis of the largest verb database VerbNet. The
consequences of this approach are considered.
| 2,018 | Computation and Language |
Event Representation through Semantic Roles: Evaluation of Coverage | Semantic role theory is a widely used approach for event representation. Yet,
there are multiple indications that semantic role paradigm is necessary but not
sufficient to cover all elements of event structure. We conducted an analysis
of semantic role representation for events to provide an empirical evidence of
insufficiency. The consequence of that is a hybrid role-scalar approach. The
results are considered as preliminary in investigation of semantic roles
coverage for event representation.
| 2,018 | Computation and Language |
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of
Language with Distributed Compositional Prior | We address two challenges of probabilistic topic modelling in order to better
estimate the probability of a word in a given context, i.e., P(word|context):
(1) No Language Structure in Context: Probabilistic topic models ignore word
order by summarizing a given context as a "bag-of-word" and consequently the
semantics of words in the context is lost. The LSTM-LM learns a vector-space
representation of each word by accounting for word order in local collocation
patterns and models complex characteristics of language (e.g., syntax and
semantics), while the TM simultaneously learns a latent representation from the
entire document and discovers the underlying thematic structure. We unite two
complementary paradigms of learning the meaning of word occurrences by
combining a TM (e.g., DocNADE) and a LM in a unified probabilistic framework,
named as ctx-DocNADE. (2) Limited Context and/or Smaller training corpus of
documents: In settings with a small number of word occurrences (i.e., lack of
context) in short text or data sparsity in a corpus of few documents, the
application of TMs is challenging. We address this challenge by incorporating
external knowledge into neural autoregressive topic models via a language
modelling approach: we use word embeddings as input of a LSTM-LM with the aim
to improve the word-topic mapping on a smaller and/or short-text corpus. The
proposed DocNADE extension is named as ctx-DocNADEe.
We present novel neural autoregressive topic model variants coupled with
neural LMs and embeddings priors that consistently outperform state-of-the-art
generative TMs in terms of generalization (perplexity), interpretability (topic
coherence) and applicability (retrieval and classification) over 6 long-text
and 8 short-text datasets from diverse domains.
| 2,019 | Computation and Language |
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine
Translation | This work investigates an alternative model for neural machine translation
(NMT) and proposes a novel architecture, where we employ a multi-dimensional
long short-term memory (MDLSTM) for translation modeling. In the
state-of-the-art methods, source and target sentences are treated as
one-dimensional sequences over time, while we view translation as a
two-dimensional (2D) mapping using an MDLSTM layer to define the correspondence
between source and target words. We extend beyond the current sequence to
sequence backbone NMT models to a 2D structure in which the source and target
sentences are aligned with each other in a 2D grid. Our proposed topology shows
consistent improvements over attention-based sequence to sequence model on two
WMT 2017 tasks, German$\leftrightarrow$English.
| 2,018 | Computation and Language |
Learning Noun Cases Using Sequential Neural Networks | Morphological declension, which aims to inflect nouns to indicate number,
case and gender, is an important task in natural language processing (NLP).
This research proposal seeks to address the degree to which Recurrent Neural
Networks (RNNs) are efficient in learning to decline noun cases. Given the
challenge of data sparsity in processing morphologically rich languages and
also, the flexibility of sentence structures in such languages, we believe that
modeling morphological dependencies can improve the performance of neural
network models. It is suggested to carry out various experiments to understand
the interpretable features that may lead to a better generalization of the
learned models on cross-lingual tasks.
| 2,018 | Computation and Language |
A Fast, Compact, Accurate Model for Language Identification of Codemixed
Text | We address fine-grained multilingual language identification: providing a
language code for every token in a sentence, including codemixed text
containing multiple languages. Such text is prevalent online, in documents,
social media, and message boards. We show that a feed-forward network with a
simple globally constrained decoder can accurately and rapidly label both
codemixed and monolingual text in 100 languages and 100 language pairs. This
model outperforms previously published multilingual approaches in terms of both
accuracy and speed, yielding an 800x speed-up and a 19.5% averaged absolute
gain on three codemixed datasets. It furthermore outperforms several benchmark
systems on monolingual language identification.
| 2,018 | Computation and Language |
Event Coreference Resolution Using Neural Network Classifiers | This paper presents a neural network classifier approach to detecting both
within- and cross- document event coreference effectively using only event
mention based features. Our approach does not (yet) rely on any event argument
features such as semantic roles or spatiotemporal arguments. Experimental
results on the ECB+ dataset show that our approach produces F1 scores that
significantly outperform the state-of-the-art methods for both within-document
and cross-document event coreference resolution when we use B3 and CEAFe
evaluation measures, but gets worse F1 score with the MUC measure. However,
when we use the CoNLL measure, which is the average of these three scores, our
approach has slightly better F1 for within- document event coreference
resolution but is significantly better for cross-document event coreference
resolution.
| 2,018 | Computation and Language |
Decipherment of Historical Manuscript Images | European libraries and archives are filled with enciphered manuscripts from
the early modern period. These include military and diplomatic correspondence,
records of secret societies, private letters, and so on. Although they are
enciphered with classical cryptographic algorithms, their contents are
unavailable to working historians. We therefore attack the problem of
automatically converting cipher manuscript images into plaintext. We develop
unsupervised models for character segmentation, character-image clustering, and
decipherment of cluster sequences. We experiment with both pipelined and joint
models, and we give empirical results for multiple ciphers.
| 2,019 | Computation and Language |
Improving Neural Text Simplification Model with Simplified Corpora | Text simplification (TS) can be viewed as monolingual translation task,
translating between text variations within a single language. Recent neural TS
models draw on insights from neural machine translation to learn lexical
simplification and content reduction using encoder-decoder model. But different
from neural machine translation, we cannot obtain enough ordinary and
simplified sentence pairs for TS, which are expensive and time-consuming to
build. Target-side simplified sentences plays an important role in boosting
fluency for statistical TS, and we investigate the use of simplified sentences
to train, with no changes to the network architecture. We propose to pair
simple training sentence with a synthetic ordinary sentence via
back-translation, and treating this synthetic data as additional training data.
We train encoder-decoder model using synthetic sentence pairs and original
sentence pairs, which can obtain substantial improvements on the available
WikiLarge data and WikiSmall data compared with the state-of-the-art methods.
| 2,018 | Computation and Language |
New Vistas to study Bhartrhari: Cognitive NLP | The Sanskrit grammatical tradition which has commenced with Panini's
Astadhyayi mostly as a Padasastra has culminated as a Vakyasastra, at the hands
of Bhartrhari. The grammarian-philosopher Bhartrhari and his authoritative work
'Vakyapadiya' have been a matter of study for modern scholars, at least for
more than 50 years, since Ashok Aklujkar submitted his Ph.D. dissertation at
Harvard University. The notions of a sentence and a word as a meaningful
linguistic unit in the language have been a subject matter for the discussion
in many works that followed later on. While some scholars have applied
philological techniques to critically establish the text of the works of
Bhartrhari, some others have devoted themselves to exploring philosophical
insights from them. Some others have studied his works from the point of view
of modern linguistics, and psychology. Few others have tried to justify the
views by logical discussions.
In this paper, we present a fresh view to study Bhartrhari, and his works,
especially the 'Vakyapadiya'. This view is from the field of Natural Language
Processing (NLP), more specifically, what is called as Cognitive NLP. We have
studied the definitions of a sentence given by Bhartrhari at the beginning of
the second chapter of 'Vakyapadiya'. We have researched one of these
definitions by conducting an experiment and following the methodology of
silent-reading of Sanskrit paragraphs. We collect the Gaze-behavior data of
participants and analyze it to understand the underlying comprehension
procedure in the human mind and present our results. We evaluate the
statistical significance of our results using T-test, and discuss the caveats
of our work. We also present some general remarks on this experiment and
usefulness of this method for gaining more insights in the work of Bhartrhari.
| 2,018 | Computation and Language |
Is there Gender bias and stereotype in Portuguese Word Embeddings? | In this work, we propose an analysis of the presence of gender bias
associated with professions in Portuguese word embeddings. The objective of
this work is to study gender implications related to stereotyped professions
for women and men in the context of the Portuguese language.
| 2,018 | Computation and Language |
Extracting Arguments from Korean Question and Command: An Annotated
Corpus for Structured Paraphrasing | Intention identification is a core issue in dialog management. However, due
to the non-canonicality of the spoken language, it is difficult to extract the
content automatically from the conversation-style utterances. This is much more
challenging for languages like Korean and Japanese since the agglutination
between morphemes make it difficult for the machines to parse the sentence and
understand the intention. To suggest a guideline for this problem, and to merge
the issue flexibly with the neural paraphrasing systems introduced recently, we
propose a structured annotation scheme for Korean question/commands and the
resulting corpus which are widely applicable to the field of argument mining.
The scheme and dataset are expected to help machines understand the intention
of natural language and grasp the core meaning of conversation-style
instructions.
| 2,019 | Computation and Language |
Multimodal Speech Emotion Recognition Using Audio and Text | Speech emotion recognition is a challenging task, and extensive reliance has
been placed on models that use audio features in building well-performing
classifiers. In this paper, we propose a novel deep dual recurrent encoder
model that utilizes text data and audio signals simultaneously to obtain a
better understanding of speech data. As emotional dialogue is composed of sound
and spoken content, our model encodes the information from audio and text
sequences using dual recurrent neural networks (RNNs) and then combines the
information from these sources to predict the emotion class. This architecture
analyzes speech data from the signal level to the language level, and it thus
utilizes the information within the data more comprehensively than models that
focus on audio features. Extensive experiments are conducted to investigate the
efficacy and properties of the proposed model. Our proposed model outperforms
previous state-of-the-art methods in assigning data to one of four emotion
categories (i.e., angry, happy, sad and neutral) when the model is applied to
the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.
| 2,018 | Computation and Language |
End-to-End Content and Plan Selection for Data-to-Text Generation | Learning to generate fluent natural language from structured data with neural
networks has become an common approach for NLG. This problem can be challenging
when the form of the structured data varies between examples. This paper
presents a survey of several extensions to sequence-to-sequence models to
account for the latent content selection process, particularly variants of copy
attention and coverage decoding. We further propose a training method based on
diverse ensembling to encourage models to learn distinct sentence templates
during training. An empirical evaluation of these techniques shows an increase
in the quality of generated text across five automated metrics, as well as
human evaluation.
| 2,018 | Computation and Language |
BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding | We introduce a new language representation model called BERT, which stands
for Bidirectional Encoder Representations from Transformers. Unlike recent
language representation models, BERT is designed to pre-train deep
bidirectional representations from unlabeled text by jointly conditioning on
both left and right context in all layers. As a result, the pre-trained BERT
model can be fine-tuned with just one additional output layer to create
state-of-the-art models for a wide range of tasks, such as question answering
and language inference, without substantial task-specific architecture
modifications.
BERT is conceptually simple and empirically powerful. It obtains new
state-of-the-art results on eleven natural language processing tasks, including
pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering
Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1
(5.1 point absolute improvement).
| 2,019 | Computation and Language |
Eyes are the Windows to the Soul: Predicting the Rating of Text Quality
Using Gaze Behaviour | Predicting a reader's rating of text quality is a challenging task that
involves estimating different subjective aspects of the text, like structure,
clarity, etc. Such subjective aspects are better handled using cognitive
information. One such source of cognitive information is gaze behaviour. In
this paper, we show that gaze behaviour does indeed help in effectively
predicting the rating of text quality. To do this, we first model text quality
as a function of three properties - organization, coherence and cohesion. Then,
we demonstrate how capturing gaze behaviour helps in predicting each of these
properties, and hence the overall quality, by reporting improvements obtained
by adding gaze features to traditional textual features for score prediction.
We also hypothesize that if a reader has fully understood the text, the
corresponding gaze behaviour would give a better indication of the assigned
rating, as opposed to partial understanding. Our experiments validate this
hypothesis by showing greater agreement between the given rating and the
predicted rating when the reader has a full understanding of the text.
| 2,018 | Computation and Language |
Sequence-to-Sequence Models for Data-to-Text Natural Language
Generation: Word- vs. Character-based Processing and Output Diversity | We present a comparison of word-based and character-based
sequence-to-sequence models for data-to-text natural language generation, which
generate natural language descriptions for structured inputs. On the datasets
of two recent generation challenges, our models achieve comparable or better
automatic evaluation results than the best challenge submissions. Subsequent
detailed statistical and human analyses shed light on the differences between
the two input representations and the diversity of the generated texts. In a
controlled experiment with synthetic training data generated from templates, we
demonstrate the ability of neural models to learn novel combinations of the
templates and thereby generalize beyond the linguistic structures they were
trained on.
| 2,018 | Computation and Language |
Towards Understanding Linear Word Analogies | A surprising property of word vectors is that word analogies can often be
solved with vector arithmetic. However, it is unclear why arithmetic operators
correspond to non-linear embedding models such as skip-gram with negative
sampling (SGNS). We provide a formal explanation of this phenomenon without
making the strong assumptions that past theories have made about the vector
space and word distribution. Our theory has several implications. Past work has
conjectured that linear substructures exist in vector spaces because relations
can be represented as ratios; we prove that this holds for SGNS. We provide
novel justification for the addition of SGNS word vectors by showing that it
automatically down-weights the more frequent word, as weighting schemes do ad
hoc. Lastly, we offer an information theoretic interpretation of Euclidean
distance in vector spaces, justifying its use in capturing word dissimilarity.
| 2,019 | Computation and Language |
Semantic Structural Evaluation for Text Simplification | Current measures for evaluating text simplification systems focus on
evaluating lexical text aspects, neglecting its structural aspects. In this
paper we propose the first measure to address structural aspects of text
simplification, called SAMSA. It leverages recent advances in semantic parsing
to assess simplification quality by decomposing the input based on its semantic
structure and comparing it to the output. SAMSA provides a reference-less
automatic evaluation procedure, avoiding the problems that reference-based
methods face due to the vast space of valid simplifications for a given
sentence. Our human evaluation experiments show both SAMSA's substantial
correlation with human judgments, as well as the deficiency of existing
reference-based measures in evaluating structural simplification.
| 2,018 | Computation and Language |
Neural Relation Extraction Within and Across Sentence Boundaries | Past work in relation extraction mostly focuses on binary relation between
entity pairs within single sentence. Recently, the NLP community has gained
interest in relation extraction in entity pairs spanning multiple sentences. In
this paper, we propose a novel architecture for this task: inter-sentential
dependency-based neural networks (iDepNN). iDepNN models the shortest and
augmented dependency paths via recurrent and recursive neural networks to
extract relationships within (intra-) and across (inter-) sentence boundaries.
Compared to SVM and neural network baselines, iDepNN is more robust to false
positives in relationships spanning sentences.
We evaluate our models on four datasets from newswire (MUC6) and medical
(BioNLP shared task) domains that achieve state-of-the-art performance and show
a better balance in precision and recall for inter-sentential relationships. We
perform better than 11 teams participating in the BioNLP shared task 2016 and
achieve a gain of 5.2% (0.587 vs 0.558) in F1 over the winning team. We also
release the crosssentence annotations for MUC6.
| 2,019 | Computation and Language |
Simple and Effective Text Simplification Using Semantic and Neural
Methods | Sentence splitting is a major simplification operator. Here we present a
simple and efficient splitting algorithm based on an automatic semantic parser.
After splitting, the text is amenable for further fine-tuned simplification
operations. In particular, we show that neural Machine Translation can be
effectively used in this situation. Previous application of Machine Translation
for simplification suffers from a considerable disadvantage in that they are
over-conservative, often failing to modify the source in any way. Splitting
based on semantic parsing, as proposed here, alleviates this issue. Extensive
automatic and human evaluation shows that the proposed method compares
favorably to the state-of-the-art in combined lexical and structural
simplification.
| 2,018 | Computation and Language |
Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns | Coreference resolution is an important task for natural language
understanding, and the resolution of ambiguous pronouns a longstanding
challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in
sufficient volume or diversity to accurately indicate the practical utility of
models. Furthermore, we find gender bias in existing corpora and systems
favoring masculine entities. To address this, we present and release GAP, a
gender-balanced labeled corpus of 8,908 ambiguous pronoun-name pairs sampled to
provide diverse coverage of challenges posed by real-world text. We explore a
range of baselines which demonstrate the complexity of the challenge, the best
achieving just 66.9% F1. We show that syntactic structure and continuous neural
models provide promising, complementary cues for approaching the challenge.
| 2,018 | Computation and Language |
SyntaxSQLNet: Syntax Tree Networks for Complex and
Cross-DomainText-to-SQL Task | Most existing studies in text-to-SQL tasks do not require generating complex
SQL queries with multiple clauses or sub-queries, and generalizing to new,
unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network
to address the complex and cross-domain text-to-SQL generation task.
SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL
generation path history and table-aware column attention encoders. We evaluate
SyntaxSQLNet on the Spider text-to-SQL task, which contains databases with
multiple tables and complex SQL queries with multiple SQL clauses and nested
queries. We use a database split setting where databases in the test set are
unseen during training. Experimental results show that SyntaxSQLNet can handle
a significantly greater number of complex SQL examples than prior work,
outperforming the previous state-of-the-art model by 7.3% in exact matching
accuracy. We also show that SyntaxSQLNet can further improve the performance by
an additional 7.5% using a cross-domain augmentation method, resulting in a
14.8% improvement in total. To our knowledge, we are the first to study this
complex and cross-domain text-to-SQL task.
| 2,018 | Computation and Language |
One Size Does Not Fit All: Generating and Evaluating Variable Number of
Keyphrases | Different texts shall by nature correspond to different number of keyphrases.
This desideratum is largely missing from existing neural keyphrase generation
models. In this study, we address this problem from both modeling and
evaluation perspectives.
We first propose a recurrent generative model that generates multiple
keyphrases as delimiter-separated sequences. Generation diversity is further
enhanced with two novel techniques by manipulating decoder hidden states. In
contrast to previous approaches, our model is capable of generating diverse
keyphrases and controlling number of outputs.
We further propose two evaluation metrics tailored towards the
variable-number generation. We also introduce a new dataset StackEx that
expands beyond the only existing genre (i.e., academic writing) in keyphrase
generation tasks. With both previous and new evaluation metrics, our model
outperforms strong baselines on all datasets.
| 2,020 | Computation and Language |
Important Attribute Identification in Knowledge Graph | The knowledge graph(KG) composed of entities with their descriptions and
attributes, and relationship between entities, is finding more and more
application scenarios in various natural language processing tasks. In a
typical knowledge graph like Wikidata, entities usually have a large number of
attributes, but it is difficult to know which ones are important. The
importance of attributes can be a valuable piece of information in various
applications spanning from information retrieval to natural language
generation. In this paper, we propose a general method of using external user
generated text data to evaluate the relative importance of an entity's
attributes. To be more specific, we use the word/sub-word embedding techniques
to match the external textual data back to entities' attribute name and values
and rank the attributes by their matching cohesiveness. To our best knowledge,
this is the first work of applying vector based semantic matching to important
attribute identification, and our method outperforms the previous traditional
methods. We also apply the outcome of the detected important attributes to a
language generation task; compared with previous generated text, the new method
generates much more customized and informative messages.
| 2,018 | Computation and Language |
IndoSum: A New Benchmark Dataset for Indonesian Text Summarization | Automatic text summarization is generally considered as a challenging task in
the NLP community. One of the challenges is the publicly available and large
dataset that is relatively rare and difficult to construct. The problem is even
worse for low-resource languages such as Indonesian. In this paper, we present
IndoSum, a new benchmark dataset for Indonesian text summarization. The dataset
consists of news articles and manually constructed summaries. Notably, the
dataset is almost 200x larger than the previous Indonesian summarization
dataset of the same domain. We evaluated various extractive summarization
approaches and obtained encouraging results which demonstrate the usefulness of
the dataset and provide baselines for future research. The code and the dataset
are available online under permissive licenses.
| 2,019 | Computation and Language |
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical
Diversity of Documents | A high degree of topical diversity is often considered to be an important
characteristic of interesting text documents. A recent proposal for measuring
topical diversity identifies three distributions for assessing the diversity of
documents: distributions of words within documents, words within topics, and
topics within documents. Topic models play a central role in this approach and,
hence, their quality is crucial to the efficacy of measuring topical diversity.
The quality of topic models is affected by two causes: generality and impurity
of topics. General topics only include common information of a background
corpus and are assigned to most of the documents. Impure topics contain words
that are not related to the topic. Impurity lowers the interpretability of
topic models. Impure topics are likely to get assigned to documents
erroneously. We propose a hierarchical re-estimation process aimed at removing
generality and impurity. Our approach has three re-estimation components: (1)
document re-estimation, which removes general words from the documents; (2)
topic re-estimation, which re-estimates the distribution over words of each
topic; and (3) topic assignment re-estimation, which re-estimates for each
document its distributions over topics. For measuring topical diversity of text
documents, our HiTR approach improves over the state-of-the-art measured on
PubMed dataset.
| 2,018 | Computation and Language |
Building Dynamic Knowledge Graphs from Text using Machine Reading
Comprehension | We propose a neural machine-reading model that constructs dynamic knowledge
graphs from procedural text. It builds these graphs recurrently for each step
of the described procedure, and uses them to track the evolving states of
participant entities. We harness and extend a recently proposed machine reading
comprehension (MRC) model to query for entity states, since these states are
generally communicated in spans of text and MRC models perform well in
extracting entity-centric spans. The explicit, structured, and evolving
knowledge graph representations that our model constructs can be used in
downstream question answering tasks to improve machine comprehension of text,
as we demonstrate empirically. On two comprehension tasks from the recently
proposed PROPARA dataset (Dalvi et al., 2018), our model achieves
state-of-the-art results. We further show that our model is competitive on the
RECIPES dataset (Kiddon et al., 2015), suggesting it may be generally
applicable. We present some evidence that the model's knowledge graphs help it
to impose commonsense constraints on its predictions.
| 2,018 | Computation and Language |
MeanSum: A Neural Model for Unsupervised Multi-document Abstractive
Summarization | Abstractive summarization has been studied using neural sequence transduction
methods with datasets of large, paired document-summary examples. However, such
datasets are rare and the models trained from them do not generalize to other
domains. Recently, some progress has been made in learning sequence-to-sequence
mappings with only unpaired examples. In our work, we consider the setting
where there are only documents (product or business reviews) with no summaries
provided, and propose an end-to-end, neural model architecture to perform
unsupervised abstractive summarization. Our proposed model consists of an
auto-encoder where the mean of the representations of the input reviews decodes
to a reasonable summary-review while not relying on any review-specific
features. We consider variants of the proposed architecture and perform an
ablation study to show the importance of specific components. We show through
automated metrics and human evaluation that the generated summaries are highly
abstractive, fluent, relevant, and representative of the average sentiment of
the input reviews. Finally, we collect a reference evaluation dataset and show
that our model outperforms a strong extractive baseline.
| 2,019 | Computation and Language |
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